Aims
Subclassification of large B cell lymphoma (LBCL) is challenging due to the overlap in histopathological, immunophenotypical and genetic data. In particular, the criteria to separate diffuse ...large B cell lymphoma (DLBCL) and high‐grade B cell lymphoma (HGBL) are difficult to apply in practice. The Lunenburg Lymphoma Biomarker Consortium previously reported a cohort of over 5000 LBCL that included fluorescence in‐situ hybridisation (FISH) data. This cohort contained 209 cases with MYC rearrangement that were available for a validation study by a panel of eight expert haematopathologists of how various histopathological features are used.
Methods and results
Digital whole slide images of haematoxylin and eosin‐stained sections allowed the pathologists to visually score cases independently as well as participate in virtual joint review conferences. Standardised consensus guidelines were formulated for scoring histopathological features and included overall architecture/growth pattern, presence or absence of a starry‐sky pattern, cell size, nuclear pleomorphism, nucleolar prominence and a range of cytological characteristics. Despite the use of consensus guidelines, the results show a high degree of discordance among the eight expert pathologists. Approximately 50% of the cases lacked a majority score, and this discordance spanned all six histopathological features. Moreover, none of the histological variables aided in prediction of MYC single versus double/triple‐hit or immunoglobulin‐partner FISH‐based designations or clinical outcome measures.
Conclusions
Our findings indicate that there are no specific conventional morphological parameters that help to subclassify MYC‐rearranged LBCL or select cases for FISH analysis, and that incorporation of FISH data is essential for accurate classification and prognostication.
Graph pattern matching has been widely used in a broad spectrum of real-world applications, and it has been the subject of several investigations, mainly of its importance and use. In this context, ...different models along with their appropriate algorithms have been proposed. However, in addition to the excessive processing costs, most of the existing models suffer from the failing query problem due to their limitations on finding meaningful matches. Also, in some scenarios, the number of matches may be enormous, making the inspection a daunting task. In this work, we introduce a new model for graph pattern matching, called
relaxed graph simulation
(
RGS
), allowing the relaxation of queries to identify more significant matches and to avoid the empty-set answer problem. We then formalize and study the top-
k
matching problem based on two function classes, relevance and diversity, for ranking the matches with respect to the proposed model. We also formalize and investigate the diversified top-
k
matching problem, and we propose a diversification function to balance relevance and diversity. Nonetheless, we provide efficient algorithms based on optimization strategies to compute the top-
k
and the diversified top-
k
matches according to the
RGS
model. Our experimental results, on four real datasets, demonstrate both the effectiveness and the efficiency of the proposed approaches.
Ces dernières années ont connu un regain d'intérêt pour l'utilisation des graphes comme moyen fiable de représentation et de modélisation des données, et ce, dans divers domaines de l'informatique. ...En particulier, pour les grandes masses de données, les graphes apparaissent comme une alternative prometteuse aux bases de données relationnelles. Plus particulièrement, le recherche de sous-graphes s'avère être une tâche cruciale pour explorer ces grands jeux de données. Dans cette thèse, nous étudions deux problématiques principales. Dans un premier temps, nous abordons le problème de la détection de motifs dans les grands graphes. Ce problème vise à rechercher les k-meilleures correspondances (top-k) d'un graphe motif dans un graphe de données. Pour cette problématique, nous introduisons un nouveau modèle de détection de motifs de graphe nommé la Simulation Relaxée de Graphe (RGS), qui permet d’identifier des correspondances de graphes avec un certain écart (structurel) et ainsi éviter le problème de réponse vide. Ensuite, nous formalisons et étudions le problème de la recherche des k-meilleures réponses suivant deux critères, la pertinence (la meilleure similarité entre le motif et les réponses) et la diversité (la dissimilarité entre les réponses). Nous considérons également le problème des k-meilleures correspondances diversifiées et nous proposons une fonction de diversification pour équilibrer la pertinence et la diversité. En outre, nous développons des algorithmes efficaces basés sur des stratégies d’optimisation en respectant le modèle proposé. Notre approche est efficiente en terme de temps d’exécution et flexible en terme d'applicabilité. L’analyse de la complexité des algorithmes et les expérimentations menées sur des jeux de données réelles montrent l’efficacité des approches proposées. Dans un second temps, nous abordons le problème de recherche agrégative dans des documents XML. Pour un arbre requête, l'objectif est de trouver des motifs correspondants dans un ou plusieurs documents XML et de les agréger dans un seul agrégat. Dans un premier temps nous présentons la motivation derrière ce paradigme de recherche agrégative et nous expliquons les gains potentiels par rapport aux méthodes classiques de requêtage. Ensuite nous proposons une nouvelle approche qui a pour but de construire, dans la mesure du possible, une réponse cohérente et plus complète en agrégeant plusieurs résultats provenant de plusieurs sources de données. Les expérimentations réalisées sur plusieurs ensembles de données réelles montrent l’efficacité de cette approche en termes de pertinence et de qualité de résultat.
Recent years have witnessed a growing renewed interest in the use of graphs as a reliable means for representing and modeling data. Thereby, graphs enable to ensure efficiency in various fields of computer science, especially for massive data where graphs arise as a promising alternative to relational databases for big data modeling. In this regard, querying data graph proves to be a crucial task to explore the knowledge in these datasets. In this dissertation, we investigate two main problems. In the first part we address the problem of detecting patterns in larger graphs, called the top-k graph pattern matching problem. We introduce a new graph pattern matching model named Relaxed Graph Simulation (RGS), to identify significant matches and to avoid the empty-set answer problem. We formalize and study the top-k matching problem based on two classes of functions, relevance and diversity, for ranking the matches according to the RGS model. We also consider the diversified top-k matching problem, and we propose a diversification function to balance relevance and diversity. Moreover, we provide efficient algorithms based on optimization strategies to compute the top-k and the diversified top-k matches according to the proposed model. The proposed approach is optimal in terms of search time and flexible in terms of applicability. The analyze of the time complexity of the proposed algorithms and the extensive experiments on real-life datasets demonstrate both the effectiveness and the efficiency of these approaches. In the second part, we tackle the problem of graph querying using aggregated search paradigm. We consider this problem for particular types of graphs that are trees, and we deal with the query processing in XML documents. Firstly, we give the motivation behind the use of such a paradigm, and we explain the potential benefits compared to traditional querying approaches. Furthermore, we propose a new method for aggregated tree search, based on approximate tree matching algorithm on several tree fragments, that aims to build, the extent possible, a coherent and complete answer by combining several results. The proposed solutions are shown to be efficient in terms of relevance and quality on different real-life datasets
Aims Subclassification of large B cell lymphoma (LBCL) is challenging due to the overlap in histopathological, immunophenotypical and genetic data. In particular, the criteria to separate diffuse ...large B cell lymphoma (DLBCL) and high‐grade B cell lymphoma (HGBL) are difficult to apply in practice. The Lunenburg Lymphoma Biomarker Consortium previously reported a cohort of over 5000 LBCL that included fluorescence in‐situ hybridisation (FISH) data. This cohort contained 209 cases with MYC rearrangement that were available for a validation study by a panel of eight expert haematopathologists of how various histopathological features are used. Methods and results Digital whole slide images of haematoxylin and eosin‐stained sections allowed the pathologists to visually score cases independently as well as participate in virtual joint review conferences. Standardised consensus guidelines were formulated for scoring histopathological features and included overall architecture/growth pattern, presence or absence of a starry‐sky pattern, cell size, nuclear pleomorphism, nucleolar prominence and a range of cytological characteristics. Despite the use of consensus guidelines, the results show a high degree of discordance among the eight expert pathologists. Approximately 50% of the cases lacked a majority score, and this discordance spanned all six histopathological features. Moreover, none of the histological variables aided in prediction of MYC single versus double/triple‐hit or immunoglobulin‐partner FISH‐based designations or clinical outcome measures. Conclusions Our findings indicate that there are no specific conventional morphological parameters that help to subclassify MYC ‐rearranged LBCL or select cases for FISH analysis, and that incorporation of FISH data is essential for accurate classification and prognostication.
Fast Top-k Search with Relaxed Graph Simulation Habi, Abdelmalek; Effantin, Brice; Kheddouci, Hamamache
2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM),
2018-Aug.
Conference Proceeding
Graph pattern matching has been widely used in large spectrum of real applications. In this context, different models along with their appropriate algorithms have been proposed. However, a major ...drawback on existing models is their limitation to find meaningful matches resulting in a number of failing queries. In this paper we introduce a new model for graph pattern matching allowing the relaxation of queries in order to avoid the empty-answer problem. Then we develop an efficient algorithm based on optimization strategies for computing the top k matches according to our model. Our experimental evaluation on four real datasets demonstrates both the effectiveness and the efficiency of our approach.
Fast top-k search with relaxed graph simulation Habi, Abdelmalek; Effantin, Brice; Kheddouci, Hamamache
Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining,
08/2018
Conference Proceeding
Graph pattern matching has been widely used in large spectrum of real applications. In this context, different models along with their appropriate algorithms have been proposed. However, a major ...drawback on existing models is their limitation to find meaningful matches resulting in a number of failing queries. In this paper we introduce a new model for graph pattern matching allowing the relaxation of queries in order to avoid the empty-answer problem. Then we develop an efficient algorithm based on optimization strategies for computing the top-k matches according to our model. Our experimental evaluation on four real datasets demonstrates both the effectiveness and the efficiency of our approach.
